@import tool.ReportPojo.ReportData
@import tool.ReportPojo.ReportInfo
@import views.user.report.IndexTool._
@(path: String, info: ReportInfo, resultPath: String)(implicit data: ReportData)

@getIPath3Index(i: Int) = @{
	val index = if(data.configDatas(i).isIPathExec) {
		1
	} else 0
	index
}

@getEnrich3Index(i: Int) = @{
	val index = if(data.configDatas(i).isEnrichExec) {
		1
	} else 0
	index + getIPath3Index(i)
}

@getPathway3Index(i: Int) = @{
	val index = if(data.configDatas(i).isPathwayExec) {
		1
	} else 0
	index + getEnrich3Index(i)
}

@getPathway2Index(i: Int) = @{
	if(data.configDatas(i).isIPathExec || data.configDatas(i).isEnrichExec || data.configDatas(i).isPathwayExec) {
		5
	} else 4
}

@getParCor2Index(i: Int) = @{
	val index = if(data.configDatas(i).isParCor) {
		1
	} else 0
	index + getCor2Index(i)
}

@index2Chinese(index: Int) = @{
	if(index == 2) {
		"二"
	} else if(index == 3) {
		"三"
	} else if(index == 4) {
		"四"
	} else {
		"五"
	}
}

@getResultIndex = @{
	val index = if(data.outerData.hasQc) {
		1
	} else 0
	index + 2
}

@getSummaryIndex = @{
	getResultIndex + 1
}

@getIntroIndex = @{
	getSummaryIndex + 1
}

<style>
		.nav {
			color: green;
			font: 18px "微软雅黑";
			valign: middle;
			text-align: left;
			padding-left: 15px;
			border-top: 1px solid #eee;
		}

		.singleSep {
			margin-top: 25px;
			border-bottom: 1px dashed #262626;
		}

		.doubleSep {
			margin-top: 25px;
			height: 5px;
			border-bottom: 1px dashed #262626;
			border-top: 1px dashed #262626;
		}

		.orangeColor {
			background-color: orange;
		}

		.redColor {
			background-color: red;
		}

		.darkRedColor {
			background-color: darkred;
		}

		.cyanColor {
			background-color: cyan;
		}

		.blueColor {
			background-color: blue;
		}

</style>

<!DOCTYPE html PUBLIC "-//W3C//DTD XHTML 1.0 Transitional//EN" >
<html>
	<head>
		<TITLE>代谢组分析结题报告 </TITLE>
		<META NAME="Modified" CONTENT="mengfanrui@@novogene.cn">
		<META NAME="Version" CONTENT="2014820v2.0">
		<meta charset="utf-8"/>
		<meta http-equiv="Content-Type" content="text/html; charset=utf-8" />
		<link rel="stylesheet" media="screen" href="@(path)/css/bootstrap.min.css">
		<link rel="stylesheet" type="text/css" href="@(path)/js/fancybox/jquery.fancybox.css" media="screen" />
		<link rel="stylesheet" href="@(path)/css/style.css" />
		<link rel="stylesheet" href="@(path)/css/base.css" />
		<link rel="stylesheet" href="@(path)/font-awesome-4.7.0/css/font-awesome.min.css" />
		<link rel="stylesheet" href="@(path)/PgwSlider-2.3.0/pgwslider.min.css" />
		<link rel="stylesheet" media="screen" href="@(path)/css/bootstrap-table.min.css">
		<script src="@(path)/js/jquery-3.1.1.min.js" type="text/javascript"></script>
		<script src="@(path)/js/bootstrap.min.js" type="text/javascript"></script>
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		<script type="text/javascript" src="@(path)/PgwSlider-2.3.0/pgwslider.js"></script>
		<script type="text/javascript" src="@(path)/js/fancybox/jquery.fancybox.min.js"></script>
		<script type="text/javascript" src="@(path)/js/client-jsdeps.js"></script>
		<script type="text/javascript" src="@(path)/js/client-fastopt.js"></script>
		<script src="@(path)/js/bootstrap-table.min.js" type="text/javascript"></script>
		<script src="@(path)/js/report_bootstrap-table-zh-CN.js" type="text/javascript"></script>

		@scalajs.html.scripts("client", routes.Assets.at(_).toString, name => getClass.getResource(s"/public/$name") != null)

		<style media="print">
				.noprint {
					DISPLAY: none;
				}

		</style>
		<style>
				body {
					font-family: 'Times New Roman', 宋体;
				}
		</style>
	</head>

	<body >

		<style>
				.myContainer .row div:first-child {
					padding-left: 1%;
				}

				.myContainer .row div:last-child {
					padding-right: 1%
				}
		</style>

		<nav class="navbar navbar-default navbar-fixed-top">
			<div class="container myContainer" style="width: 100%" >
				<div class="row" style="border-bottom: 2px solid #eee;
					padding-bottom: 5px;
					padding-top: 15px">
					<div class="col-sm-4">
					@if(info.isMet) {
						<img src="@(path)/images/logo.png" width="160" height="40">
						}
					</div>
					<div class="col-sm-4" style="font-size: 14px">
						提供领先的代谢组学解决方案 </br>Providing advanced metabolomics solutions
					</div>
					<div class="col-sm-4" style="text-align: right;
						font-size: 14px">
						@if(!info.isLiuyuan) {
							服务电话:@(info.phone) <br>
						}
						@if(info.isMet) {
							<a href="mailto:marketing@@metaboprofile.com" target="black" style="text-decoration: none;
								color: black;
								font-size: 14px;
								font-weight: 500;">
								Email: marketing@@metaboprofile.com</a>
						}
					</div>
				</div>

				<div class="row" style="font-family: Calibri, Arial, Helvetica, sans-serif;
					border-bottom: 2px solid #eee;
					padding-bottom: 5px;
					padding-top: 5px">
					<div class="col-sm-4">
					</div>
					<div class="col-sm-4" style="font: 20px '微软雅黑';
						color: green;">
						<b>代谢组分析结题报告</b>
					</div>
					<div class="col-sm-4" style="text-align: right">
						结题报告：<a href="@(resultPath)/../../../report.pdf" title="点击打开" target="_blank" style="text-decoration: none; ">
						PDF</a>
					</div>
				</div>

			</div>
		</nav>


		@user.report.html.left(data)
		<div id="main" style="width: 74%;
			float: right;
			position: absolute;
			right: 35px;
			top: 85px;
			overflow-y: hidden;
			overflow-x: hidden;">
			@if(info.isMet) {
				@user.report.html.metHome(path, data)
			}


			<div id="page">
				<br />
				<h2 class="titleLevel1" ><span id="项目简介">一、项目简介</span></h2>
				<p class="paragraph">
					本项目样本由客户提供，样本送达后，经拍照验收登记，即刻储存在-80°C超低温冰箱（Forma 900系列, Thermo Fisher Scientific, Nashville, NC, USA)。样本分组信息见下表。
					<br>
					<br>
					原始数据见
					<a href="@(resultPath)Preprocessed_Data/00_AllMet_Raw.csv">00_AllMet_Raw.csv</a>
					<a href="@(resultPath)Preprocessed_Data" target="_blank"><span class="fa fa-folder-open"></span></a>
					<br>
					经归一化等前处理的数据见<a href="@(resultPath)Preprocessed_Data/04_AllMet.csv">04_AllMet.csv</a>
					<a href="@(resultPath)Preprocessed_Data" target="_blank"><span class="fa fa-folder-open"></span></a>
				</p>
				<p class="name_table">　样本分组信息表</p>


				<div class="table-responsive">
					<table class="tf1 myTable" id="sampleInfoTable" data-pagination="true" data-page-list="[10, 25, 50, 100, all]"
					data-search="true">
						<thead>
							<tr>
							@for(header <- data.sampleInfo.headers) {
								<th>@header</th>
							}
							</tr>
						</thead>
						<tbody>
						@for(row <- data.sampleInfo.rows) {
							<tr>
							@for(col <- row) {
								<td>@col</td>
							}
							</tr>
						}
						</tbody>

					</table>
				</div>
				<div class="doubleSep"></div>
			</div>

			@if(data.outerData.hasQc) {
				<div id="page">
					<br />

					<h2 class="titleLevel1"><span id="质量控制">二、质量控制</span></h2>
					<p class="paragraph">
						质量控制（Quality control，QC）样本是所有待上机样本混合获得，理论上，QC样本中所含代谢物的种类和丰度差异应当在随机误差范围内。因此评估QC样本检测结果的一致性可以作为实验室自动化作业和质量管理的一种重要手段。
					</p>

					<h3 id="qcBasic" class="myBasic titleLevel2"><span id="多变量质控图">1　多变量质控图</span></h3>
					<p class="paragraph">
						<b>多变量质量控制图</b>
						(Multivariate Control Chart，见图1) 是依据样本数据形成的样本得分点位置以及变化趋势进行分析和判断。可用于自动监控和判断实验室样本制备、自动化衍生和样本分析过程是否处于可控的状态。在控制图中，样本点超出控制界限（均值±3倍标准差）被认为是离群点；连续七个以上样本点在中心线的2倍标准差上波动时要及时暂停或停止操作，分析原因后再进行样本测试。
					</p>
					<p class="name_table">图1　多变量质控图</p>
					<div align="center">
						<img class="wid2" src="@(resultPath)Quality_Control/01_PC1_with_All_Samples/PC1_Dotplot.png">
					</div>
					@user.report.qc(resultPath)


				</div>
					<!----------------------------------------- QC样本间相关性 -------------------------------------->
				<div id="page">
					<br />
					<h3 class="titleLevel2" id="QC样本间相关性">
						<span id="qc02" class="myMain">
							2　QC样本间相关性
						</span>
					</h3>
					@user.report.qc2(resultPath)
					<p class="name_table">图2　QC样本相关性热图</p>
					<div align="center">
						<img class="wid2" src="@(resultPath)Quality_Control/02_QC_Correlation_Pearson/QC_Pearson_Correlation_Heatmap.png" >
					</div>

				</div>

					<!----------------------------------------- 样本代谢轮廓变异 -------------------------------------->
				<div id="page">
					<br />
					<h3 class="titleLevel2" id="样本代谢轮廓变异">
						<span id="qc03" class="myMain">
							3　样本代谢轮廓变异
						</span>
					</h3>

					<p class="paragraph">
						图3为带有QC样本的主成分分析（Principal Component Analysis，PCA）得分图，图中QC样本点彼此靠近、聚集程度越高，提示仪器检测的稳定性越好。PCA分析本身属于无监督的方法，有助于观察数据本身的特征。
					</p>
					<p class="name_table">　图  3 主成分分析得分图</p>
					<p class="center">
						<img class="wid2" src="@(resultPath)Quality_Control/01_PC1_with_All_Samples/PCA_with_All_Samples_First2PCs.png" >
					</p>

				</div>

				<div id="page">
					<br />

					<h3 class="titleLevel2" id="代谢物鉴定及注释情况">
						<span id="qc04" class="myMain"> 4　代谢物鉴定及注释情况</span>
					</h3>
					@user.report.metAnno(resultPath, data)

					<div class="doubleSep"></div>

				</div>

			}


			<br />
			<h2 class="titleLevel1"><span name="分析结果"> @(index2Chinese(getResultIndex))、分析结果</span></h2>

			@for(i <- data.treats.indices) {
				<h3 class="titleLevel2" id="file_dir">
					@(i + 1) @data.treats(i)
				</h3>

				<h4 id="@(data.treats(i))Basic" class="myMain myBasic titleLevel3">
					<span id="@(data.treats(i))代谢物分类总览" class="myLink">
						@(i + 1).1 代谢物分类总览
					</span>
				</h4>

				<div id="page">
					@user.report.metClassify(resultPath, data, i)

				<p class="name_table">　图 @(i + 1)-1  代谢物分类情况</p>
				<div class="row">
					<div class="col-sm-12">
						<p class="imgP"><span>A</span></p>
						<ul class="pgwSlider">
							<li>
								<img class="myImg" src="@(resultPath)Treatment/@(data.treats(i))/01_Basic_Statistics/Metabolite_Class_Statistics/Class_Barplot_by_Group.png"
								alt="A">
							</li>
							<li>
								<img class="myImg" src="@(resultPath)Treatment/@(data.treats(i))/01_Basic_Statistics/Metabolite_Class_Statistics/Class_Barplot_by_Sample.png"
								alt="B">
							</li>
						</ul>
					</div>
				</div>
				</div>

				<div class="row">
					<div class="col-sm-12">
						<h4 id="@(data.treats(i))Mul" class="myMain titleLevel3">
							<span id="@(data.treats(i))多维统计分析">
								@(i + 1).2 多维统计分析
							</span>
						</h4>
					</div>

				</div>


				<div id="page">
					<br />
					<h5 class="titleLevel4">@(i + 1).2.1 主成分分析</h5>
					<p class="paragraph">
					图 @(i + 1)-2 A和图 @(i + 1)-2 B分别展示了2D主成分得分图和3D主成分得分图。
					</p>
					<p class="paragraph">
					原始主成分得分见
						<a href="@(resultPath)Treatment/@(data.treats(i))/02_PCA/All_Groups/PCA_Score.csv">PCA_Score.csv</a>
						<a href="@(resultPath)Treatment/@(data.treats(i))/02_PCA/All_Groups" target="_blank"><span class="fa fa-folder-open"></span></a>
					</p>
					<p class="paragraph">
					带有样本名称标记的主成分得分图见
						<a href="@(resultPath)Treatment/@(data.treats(i))/02_PCA/All_Groups/PCA_Score_2D_Label.pdf" target="_blank">PCA_Score_2D_Label.pdf</a>
						<a href="@(resultPath)Treatment/@(data.treats(i))/02_PCA/All_Groups" target="_blank"><span class="fa fa-folder-open"></span></a>
					</p>
					<p class="paragraph">
					更多主成分组合见
						<a href="@(resultPath)Treatment/@(data.treats(i))/02_PCA/All_Groups/All_Combinaiton/All_PCs_Pairsplot.pdf" target="_blank">
					All_PCs_Pairsplot.pdf
						</a>
						<a href="@(resultPath)Treatment/@(data.treats(i))/02_PCA/All_Groups/All_Combinaiton" target="_blank"><span class="fa fa-folder-open"></span></a>
					</p>
					<p class="name_table">　图 @(i + 1)-2 样品PCA得分图</p>
					<p class="imgP"><span>A</span></p>
					<ul class="pgwSlider">
						<li>
							<img class="myImg" src="@(resultPath)Treatment/@(data.treats(i))/02_PCA/All_Groups/PCA_Score_2D.png"
							alt="A">
						</li>
						<li>
							<img class="myImg" src="@(resultPath)Treatment/@(data.treats(i))/02_PCA/All_Groups/PC123_Score_3D.png"
							alt="B">
						</li>
					</ul>

					<div class="row">
						<div class="col-sm-12">
							<p class="paragraph">
					图 @(i + 1)-3展示了被分析样本的2D主成分得分图以及对应主成分得分的箱式图。当分组较多时（3组以上），所有点聚集在2D主成分得分图上，可能会造成解读的不便。将每一组的所有样本在对应主成分的得分绘制成箱式图，便于我们更直观地观察多组数据的平均水平和变异程度。
							</p>
						</div>

					</div>

					<p class="name_table">　图 @(i + 1)-3 PCA得分图及对应主成分箱线图</p>
					<p class="center">
						<img class="wid2" src="@(resultPath)Treatment/@(data.treats(i))/02_PCA/All_Groups/PCA_Score_with_Boxplot_with_Points.png" />
					</p>
					<p class="paragraph">
						将各组样本数据分别使用PCA建模，获取各个独立分组的主成分得分图见文件夹
						<a target="_blank" href="@(resultPath)Treatment/@(data.treats(i))/02_PCA/Uni_Group">Uni_Group</a>
						。这种组内PCA 分析摒弃了分组信息的干扰，能够更清晰地观察组内变异并找出可能的离群点（outlier）。
					</p>
				</div>

				<div id="page">
					<br />
					<h5 class="titleLevel4">@(i + 1).2.2  PLS-DA分析</h5>
					<p class="paragraph">
					PLS-DA原始数据见
						<a href="@(resultPath)Treatment/@(data.treats(i))/03_PLS_DA/PLSDA_Score.csv">PLSDA_Score.csv</a>
						<a href="@(resultPath)Treatment/@(data.treats(i))/03_PLS_DA" target="_blank"><span class="fa fa-folder-open"></span></a>
					</p>
					<p class="paragraph">
					PLS-DA 的2D得分图见
						<a href="@(resultPath)Treatment/@(data.treats(i))/03_PLS_DA/PLSDA_Score_2D.pdf" target="_blank">PLSDA_Score_2D.pdf</a>
						<a href="@(resultPath)Treatment/@(data.treats(i))/03_PLS_DA" target="_blank"><span class="fa fa-folder-open"></span></a>
					</p>
					<p class="paragraph">
					带有样本标签的PLS-DA得分图见
						<a href="@(resultPath)Treatment/@(data.treats(i))/03_PLS_DA/PLSDA_Score_2D_Label.pdf" target="_blank">PLSDA_Score_2D_Label.pdf</a>
						<a href="@(resultPath)Treatment/@(data.treats(i))/03_PLS_DA" target="_blank"><span class="fa fa-folder-open"></span></a>
					</p>
					<p class="paragraph">
					带有箱线图的PLS-DA得分图见
						<a href="@(resultPath)Treatment/@(data.treats(i))/03_PLS_DA/PLSDA_Score_with_Boxplot_with_Points.pdf" target="_blank">
					PLSDA_Score_with_Boxplot_with_Points.pdf
						</a>
						<a href="@(resultPath)Treatment/@(data.treats(i))/03_PLS_DA" target="_blank"><span class="fa fa-folder-open"></span></a>
					</p>

				</div>

				@if(!data.configDatas(i).isMul) {
					<div id="page">
						<br />
						<h5 class="titleLevel4">@(i + 1).2.3  OPLS-DA分析</h5>

						<p class="paragraph">
							OPLS-DA 2D得分图见图 @(i + 1)-4 A
						</p>
						<p class="paragraph">
							置换检验结果见图 @(i + 1)-4 B
						</p>
						<p class="paragraph">
							OPLS-DA得分的原始数据见
							<a href="@(resultPath)Treatment/@(data.treats(i))/04_OPLS_DA/OPLSDA_Score.csv">
								OPLSDA_Score.csv</a>
							<a href="@(resultPath)Treatment/@(data.treats(i))/04_OPLS_DA" target="_blank"><span class="fa fa-folder-open"></span></a>
						</p>
						<p class="paragraph">
							带有样本标签的OPLS-DA得分图见
							<a href="@(resultPath)Treatment/@(data.treats(i))/04_OPLS_DA/OPLSDA_Score_2D_Label.pdf" target="_blank">
								OPLSDA_Score_2D_Label.pdf
							</a>
							<a href="@(resultPath)Treatment/@(data.treats(i))/04_OPLS_DA" target="_blank"><span class="fa fa-folder-open"></span></a>
						</p>
						<p class="name_table">　图 @(i + 1)-4  OPLS-DA得分图及置换检验图</p>
						<p class="imgP"><span>A</span></p>
						<ul class="pgwSlider">
							<li>
								<img class="myImg" src="@(resultPath)Treatment/@(data.treats(i))/04_OPLS_DA/OPLSDA_Score_2D.png"
								alt="A">
							</li>
							<li>
								<img class="myImg" src="@(resultPath)Treatment/@(data.treats(i))/04_OPLS_DA/Model_Validation/OPLSDA_Permutation.png"
								alt="B">
							</li>
						</ul>

					</div>

					<div id="page">
						<br />
						<div class="row">
							<div class="col-sm-12">
								<h5 class="titleLevel4">@(i + 1).2.4  差异代谢物筛选——多维统计</h5>
							</div>
						</div>

						@user.report.oplsda(resultPath)

						<p class="paragraph">
							OPLS-DA的代谢物VIP结果数据见
							<a href="@(resultPath)Treatment/@(data.treats(i))/04_OPLS_DA/OPLSDA_VIP.csv">
								OPLSDA_VIP.csv</a>
							<a href="@(resultPath)Treatment/@(data.treats(i))/04_OPLS_DA" target="_blank"><span class="fa fa-folder-open"></span></a>
						</p>
						<p class="paragraph">
							OPLS-DA的代谢物VIP火山图见图 @(i + 1)-5
						</p>
						<p class="name_table">　图 @(i + 1)-5 OPLS-DA差异代谢物火山图</p>
						<p class="center">
							<img class="wid2" src="@(resultPath)Treatment/@(data.treats(i))/04_OPLS_DA/OPLSDA_VPlot.png" />
						</p>

					</div>

				}

				<h4 id="@(data.treats(i))Single" class="myMain titleLevel3">
					<span id="@(data.treats(i))单维统计分析">
						@(i + 1).3 单维统计分析
					</span>
				</h4>

				<div id="page">
					<br />
					<h5 class="titleLevel4">@(i + 1).3.1  代谢物Z-Score图</h5>
					<p class="paragraph">
					Z-Score原始数据见
						<a href="@(resultPath)Treatment/@(data.treats(i))/01_Basic_Statistics/Z_Score.csv">Z_Score.csv</a>
						<a href="@(resultPath)Treatment/@(data.treats(i))/01_Basic_Statistics" target="_blank"><span class="fa fa-folder-open"></span></a>
					</p>
					<p class="paragraph">
					Z-Score图见
						<a href="@(resultPath)Treatment/@(data.treats(i))/01_Basic_Statistics/Z_Score_Heatmap.pdf" target="_blank">Z_Score_Plot.pdf</a>
						<a href="@(resultPath)Treatment/@(data.treats(i))/01_Basic_Statistics" target="_blank"><span class="fa fa-folder-open"></span></a>
					</p>
				</div>

				@if(data.configDatas(i).groups.size == 2) {
					<div id="page">
						<br />
						<h5 class="titleLevel4">@(i + 1).3.2  差异代谢物筛选——单维统计</h5>
						<p class="paragraph">
							我们也可以采用单维检验（依据数据的正态性和方差齐性选取T Test或Mann-Whitney U Test）来获得两组间的差异代谢物，特别是当多维统计无法建立稳健的判别模型时（如组间样本类别分布不均衡或组内偏差过大）。
						</p>
						<p class="paragraph">
							单维代谢物火山图见图 @(i + 1)-6
						</p>
						@user.report.singleStat(resultPath, data, i)
					</div>
				} else {
					@* 多组差异代谢物筛选*@
					<div id="page">
						<br />
						<h5 class="titleLevel4">@(i + 1).3.2  差异代谢物筛选——单维统计</h5>
						<p class="paragraph">
							依据数据的正态性和方差齐性选取单维的Anova检验或Kruskal-Wallis（K-W test）检验来获得多组间的差异代谢物。
						</p>
						<p class="paragraph">
							差异代谢物阈值设定为：P < @(data.configDatas(i).pValue)
						</p>
						<p class="paragraph">
							差异代谢物的P值、基本信息、改变倍数等见
							<a href="@(resultPath)Treatment/@(data.treats(i))/@(data.resultDatas(i).uniDir)/AllMet_Test.csv">
								AllMet_Test.csv
							</a>
							<a href="@(resultPath)Treatment/@(data.treats(i))/@(data.resultDatas(i).uniDir)" target="_blank"><span class="fa fa-folder-open"></span></a>

						</p>
						<p class="paragraph">
							差异代谢物的Z Score点图见
							<a href="@(resultPath)Treatment/@(data.treats(i))/@(data.resultDatas(i).uniDir)/Z_Score_Plot.pdf" target="_blank">
								Z_Score_Plot.pdf
							</a>
							<a href="@(resultPath)Treatment/@(data.treats(i))/@(data.resultDatas(i).uniDir)" target="_blank"><span class="fa fa-folder-open"></span></a>

						</p>
						<p class="paragraph">
							单维统计分析p值排名前9的差异代谢物的箱式图，见下图 @(i + 1)-4
						</p>
						<p class="paragraph">
							全部差异代谢物的箱式图见
							<a href="@(resultPath)Treatment/@(data.treats(i))/@(data.resultDatas(i).uniDir)/AllMet_Boxplot.pdf" target="_blank">
								AllMet_Boxplot.pdf
							</a>
							<a href="@(resultPath)Treatment/@(data.treats(i))/@(data.resultDatas(i).uniDir)" target="_blank"><span class="fa fa-folder-open"></span></a>

						</p>
						<p class="paragraph">
							全部差异代谢物带原始数据点的箱式图见补充图片：
							<a href="@(resultPath)Treatment/@(data.treats(i))/@(data.resultDatas(i).uniDir)/AllMet_Boxplot_with_Points.pdf" target="_blank">
								AllMet_Boxplot_with_Points.pdf
							</a>
							<a href="@(resultPath)Treatment/@(data.treats(i))/@(data.resultDatas(i).uniDir)" target="_blank"><span class="fa fa-folder-open"></span></a>

						</p>
						<p class="paragraph">
							全部差异代谢物的小提琴图见补充图片：
							<a href="@(resultPath)Treatment/@(data.treats(i))/@(data.resultDatas(i).uniDir)/AllMet_Vioplot.pdf" target="_blank">
								AllMet_Vioplot.pdf
							</a>
							<a href="@(resultPath)Treatment/@(data.treats(i))/@(data.resultDatas(i).uniDir)" target="_blank"><span class="fa fa-folder-open"></span></a>

						</p>

						<p class="name_table">图 @(i + 1)-4 单维差异代谢物箱式图</p>
						<p class="center">
							<img class="wid2" src="@(resultPath)Treatment/@(data.treats(i))/@(data.resultDatas(i).uniDir)/AllMet_Boxplot.png" />
						</p>
					</div>
				}

				<h4 id="@(data.treats(i))Biomarker" class="myMain titleLevel3">
					<span id="@(data.treats(i))潜在生物标志物筛选">
						@(i + 1).4  潜在生物标志物筛选
					</span>
				</h4>

				<div id="page">
					<br />
					@user.report.biomarker(resultPath, data, i)
					@if(data.configDatas(i).isMul) {
						<p class="paragraph">
							这里由于是多组比较，无法进行OPLS-DA分析，所以潜在生物标志物结果与单维差异代谢物结果相同。
						</p>
					} else {


						@if(data.resultDatas(i).hasVennPlot) {

							<p class="paragraph">
								多维和单维筛选的差异代谢物的韦恩图见图 @(i + 1)-@getPotentialImageIndex(i)
							</p>
							<p class="name_table">
								图 @(i + 1)-@getPotentialImageIndex(i) 单维和多维统计筛选出的差异代谢物韦恩图
							</p>
							<p class="center">
								<img class="wid2" src="@(resultPath)Treatment/@(data.treats(i))/@(data.resultDatas(i).potentialDir)/Venn_Plot.png"/>
							</p>

						}

					}

				</div>

				<div id="page">
					<br />
					<h5 class="titleLevel4">@(i + 1).4.1  潜在生物标志物</h5>

					@if(data.resultDatas(i).markers.rows.size != 0) {
						<p class="paragraph">
							@user.report.potentialBiomarker(resultPath, data, i)
							<br>（1）单维分析 P < @(data.configDatas(i).pValue)，|log2FC| >= @(data.configDatas(i).log2FC)且
							<br>（2）多维分析VIP > @(data.configDatas(i).vip)
						</p>
						<p class="paragraph">
							所有代谢物对筛选标准的符合情况见
							<a href="@(resultPath)Treatment/@(data.treats(i))/@(data.resultDatas(i).potentialDir)/AllMet_Tests_Joined.csv">
								AllMet_Tests_Joined.csv</a>
							<a href="@(resultPath)Treatment/@(data.treats(i))/@(data.resultDatas(i).potentialDir)" target="_blank"><span class="fa fa-folder-open"></span></a>
						</p>
						<p class="paragraph">
							符合筛选标准的潜在生物标志物的原始数据见表 @(i + 1)-1
						</p>
						<p class="paragraph">
							潜在生物标志物的箱式图见
							<a href="@(resultPath)Treatment/@(data.treats(i))/@(data.resultDatas(i).potentialDir)/Markers_Boxplot_with_Points.pdf" target="_blank">
								Markers_Boxplot.pdf
							</a>
							<a href="@(resultPath)Treatment/@(data.treats(i))/@(data.resultDatas(i).potentialDir)" target="_blank"><span class="fa fa-folder-open"></span></a>
						</p>
						<p class="paragraph">
							潜在生物标志物的小提琴图见
							<a href="@(resultPath)Treatment/@(data.treats(i))/@(data.resultDatas(i).potentialDir)/Markers_Vioplot.pdf" target="_blank">
								Markers_Vioplot.pdf
							</a>
							<a href="@(resultPath)Treatment/@(data.treats(i))/@(data.resultDatas(i).potentialDir)" target="_blank"><span class="fa fa-folder-open"></span></a>
						</p>
						<p class="paragraph">
							潜在生物标志物的Z得分图见
							<a href="@(resultPath)Treatment/@(data.treats(i))/@(data.resultDatas(i).potentialDir)/Markers_Z_Score_Plot.pdf" target="_blank">
								Markers_Z_Score_Plot.pdf
							</a>
							<a href="@(resultPath)Treatment/@(data.treats(i))/@(data.resultDatas(i).potentialDir)" target="_blank"><span class="fa fa-folder-open"></span></a>
						</p>

						@user.report.heatmap(resultPath, i)

						<p class="name_table">　表 @(i + 1)-1 潜在生物标志物数据表</p>
						<table class="tf1 myTable" data-pagination="true" data-page-list="[10, 25, 50, 100, all]" data-search="true">
							<thead>
							@for(header <- data.resultDatas(i).markers.headers) {
								<th>@header</th>
							}
							</thead>
							<tbody>
							@for(row <- data.resultDatas(i).markers.rows) {
								<tr>
								@for((column, color) <- row) {
									<td class="@(color)Color" >@column</td>
								}
								</tr>
							}
							</tbody>

						</table>
					} else {
						@user.report.html.noResult()
					}

				</div>

				@if(data.configDatas(i).isIPathExec || data.configDatas(i).isEnrichExec || data.configDatas(i).isPathwayExec) {
					<h4 id="@(data.treats(i))Pathway" class="myMain titleLevel3">
						<span id="@(data.treats(i))通路分析">
							@(i + 1).@(getPathway2Index(i)) 通路分析
						</span>
					</h4>
					@if(data.resultDatas(i).markers.rows.nonEmpty) {
						<p class="paragraph">
						@user.report.modelDiff(resultPath, data, i)
						</p>
						@if(data.configDatas(i).isIPathExec) {
							<div id="page">
								<br/>
								<h5 class="titleLevel4">@(i + 1).@(getPathway2Index(i)).1 iPath通路分析</h5>
								@user.report.iPath(resultPath, i)
								<p class="name_table">图 @(i + 1)-@getIPathImageIndex(i) iPath网络图</p>
								<p class="center">
									<img class="wid2" src="@(resultPath)Treatment/@(data.treats(i))/@(data.resultDatas(i).pathwayDir)/iPath3/iPath3.png"/>
								</p>
							</div>
						}

						@if(data.configDatas(i).isEnrichExec) {
							<div id="page">
								<br />
								<h5 class="titleLevel4">@(i + 1).
									@(getPathway2Index(i)).@(getEnrich3Index(i))  通路富集分析</h5>
								@for(j <- data.configDatas(i).libTypes.indices) {

									<h6 class="titleLevel5">
										@(i + 1).
										5.@(getEnrich3Index(i)).@(j + 1) @data.configDatas(i).libTypes(j)库通路富集分析结果
									</h6>

									@if(data.resultDatas(i).hasEnrichMap.nonEmpty && data.resultDatas(i).hasEnrichMap(data.configDatas(i).libTypesAbbr(j))){
										<p class="paragraph">
											使用选定的@data.configDatas(i).libTypes(j)库对差异代谢物进行通路富集分析，结果如图 @(i + 1)-@getEnrichImageIndex(i, j)所示。
										</p>
										@user.report.enrich(resultPath, i, j)
									}else{
										<p class="paragraph">
											该分析无阳性结果，因此无法展示。
										</p>
									}
								}
							</div>
						}

						@if(data.configDatas(i).isPathwayExec) {
							<div id="page">
								<br/>
								<h5 class="titleLevel4">
									@(i + 1).
									@(getPathway2Index(i)).@(getPathway3Index(i)) 通路分析
								</h5>
								<h6 class="titleLevel5">
									@(i + 1).
									@(getPathway2Index(i)).
									@(getPathway3Index(i)).1 @(data.configDatas(i).species)库通路分析结果
								</h6>
								<p class="paragraph">
									差异代谢物在@(data.configDatas(i).species)库的通路分析结果见
									<a href="@(resultPath)Treatment/@(data.treats(i))/@(data.resultDatas(i).pathwayDir)/Pathway_Analysis/Pathway_Result.csv">
										Pathway_Result.csv
									</a>
									<a href="@(resultPath)Treatment/@(data.treats(i))/@(data.resultDatas(i).pathwayDir)/Pathway_Analysis" target="_blank"><span class="fa fa-folder-open"></span></a>
								</p>
								<p class="paragraph">
									差异代谢物在@(data.configDatas(i).species)库的通路分析条形图见
									<a href="@(resultPath)Treatment/@(data.treats(i))/@(data.resultDatas(i).pathwayDir)/Pathway_Analysis/Pathway_Barplot.pdf" target="_blank">
										Pathway_Barplot.pdf
									</a>
									<a href="@(resultPath)Treatment/@(data.treats(i))/@(data.resultDatas(i).pathwayDir)/Pathway_Analysis" target="_blank"><span class="fa fa-folder-open"></span></a>
								</p>

								@if(data.resultDatas(i).hasBubble) {
									<p class="paragraph">
										差异代谢物在@(data.configDatas(i).species)库的通路分析气泡图见图 @(i + 1)-@(getPathwayImageIndex(i))
									</p>
								}
								@user.report.pathway(resultPath, i)

								@if(data.resultDatas(i).hasBubble) {
									<p class="name_table">
										　图 @(i + 1)-@(getPathwayImageIndex(i)) @(data.configDatas(i).species)库通路分析气泡图
									</p>
									<p class="center">
										<img class="wid2" src="@(resultPath)Treatment/@(data.treats(i))/@(data.resultDatas(i).pathwayDir)/Pathway_Analysis/Pathway_Bubbleplot.png"/>
									</p>
								}
							</div>
						}
					} else {
						@user.report.html.noResult()
					}


				}

				@if(data.configDatas(i).isSelfCor || data.configDatas(i).isCor || data.configDatas(i).isParCor){
					<h4 id="@(data.treats(i))Cor" class="myMain titleLevel3">
						<span id="@(data.treats(i))Spearman相关性分析">
							@(i + 1).@(getCor2Index(i)) Spearman相关性分析
						</span>
					</h4>

					@if(data.resultDatas(i).markers.rows.nonEmpty) {
						<div id="page">
							<br />
							<p class="paragraph">
							@user.report.modelDiff(resultPath, data, i)
							</p>
							@user.report.selfCor(resultPath, i)

						</div>
						@if(data.configDatas(i).isCor) {
							@for(j <- data.outerData.extraDatas.indices) {
								<div id="page">
									<br />
									<h5 class="titleLevel4">@(i + 1).
										@(getCor2Index(i)).@(j + 2) 差异代谢物与@(data.outerData.extraDatas(j))文件相关性分析</h5>
									@user.report.uploadCor(resultPath, i, j)
								</div>
							}
						}

						@if(data.configDatas(i).isParCor) {
							<h4 id="@(data.treats(i))ParCor" class="myMain titleLevel3">
								<span id="@(data.treats(i))偏相关分析">
									@(i + 1).@(getParCor2Index(i)) 偏相关分析
								</span>
							</h4>

							<div id="page">
								<br />
								<p class="paragraph">
								@user.report.modelDiff(resultPath, data, i)
								</p>

								@user.report.parCor(resultPath, i)

							</div>

						}

					} else {
						@user.report.html.noResult()
					}

				}

				@if(data.configDatas(i).isDiagnoseExec) {

					<h4 id="@(data.treats(i))Diagnose" class="myMain titleLevel3">
						<span id="@(data.treats(i))诊断结果">
							@(i + 1).@(getDiagnose2Index(i)) 诊断结果
						</span>
					</h4>

					@if(data.resultDatas(i).markers.rows.nonEmpty) {
						<div id="page">
							<br/>
							<p class="paragraph">
							@user.report.modelDiff(resultPath, data, i)
							</p>
							<p class="paragraph">
								前面Potential_Biomarker模块中得到的，基于单维、多维中对p、FDR、VIP设定的阈值获得的差异代谢物可以作为进一步诊断实验及寻找生物标志物的候选来源。
							</p>
							@user.report.diagnoseResult(resultPath, i)

							<h5 class="titleLevel4">@(i + 1).@(getDiagnose2Index(i)).1 采用RF进行特征选择</h5>
							<p class="paragraph">
								RF重要性排序前@{data.configDatas(i).rfTop}的差异代谢物得分结果见
								<a href="@(resultPath)Treatment/@(data.treats(i))/@(data.resultDatas(i).diagnoseDir)/01_Random_Forest/RF_Top@{data.configDatas(i).rfTop}_Imp_Rank.csv">
									RF_Top@{data.configDatas(i).rfTop}_Imp_Rank.csv
								</a>
								<a href="@(resultPath)Treatment/@(data.treats(i))/@(data.resultDatas(i).diagnoseDir)/01_Random_Forest" target="_blank"><span class="fa fa-folder-open"></span></a>

							</p>
							<p class="paragraph">
								RF重要性排序前@{data.configDatas(i).rfTop}的差异代谢物得分情况见图
								@(i + 1)-@getDiagnoseImageIndex(i, 0)
							</p>
							<p class="paragraph">
								RF全部差异代谢物得分结果见
								<a href="@(resultPath)Treatment/@(data.treats(i))/@(data.resultDatas(i).diagnoseDir)/01_Random_Forest/RF_Imp_Rank.csv">
									RF_Imp_Rank.csv
								</a>
								<a href="@(resultPath)Treatment/@(data.treats(i))/@(data.resultDatas(i).diagnoseDir)/01_Random_Forest" target="_blank"><span class="fa fa-folder-open"></span></a>

							</p>
							<p class="paragraph">
								RF全部差异代谢物重要性图见
								<a href="@(resultPath)Treatment/@(data.treats(i))/@(data.resultDatas(i).diagnoseDir)/01_Random_Forest/RF_Imp.pdf" target="_blank">
									RF_Imp.pdf
								</a>
								<a href="@(resultPath)Treatment/@(data.treats(i))/@(data.resultDatas(i).diagnoseDir)/01_Random_Forest" target="_blank"><span class="fa fa-folder-open"></span></a>

							</p>
							<p class="name_table">　图 @(i + 1)-@getDiagnoseImageIndex(i, 0) RF评估代谢物重要性</p>
							<p class="center">
								<img class="wid2" src="@(resultPath)Treatment/@(data.treats(i))/@(data.resultDatas(i).diagnoseDir)/01_Random_Forest/RF_Top@{data.configDatas(i).rfTop}_Imp.png"/>
							</p>

						</div>

						@if(!data.configDatas(i).isMul) {

							<p class="paragraph">

							</p>

							<div id="page">
								<br/>
								<h5 class="titleLevel5">@(i + 1).@(getDiagnose2Index(i)).1.1 Logistic Regression模型</h5>
								<p class="paragraph">
									采用Logistic Regression模型，利用生物标志物数据和分组数据进行建模预测，结果如下：
								</p>
								<p class="paragraph">
									模型结果见文件夹<a target="_blank" href="@(resultPath)Treatment/@(data.treats(i))/@(data.resultDatas(i).diagnoseDir)/01_Random_Forest/Diagnose_Model_Generation/Logistic_Regression">
									Logistic_Regression</a>
								</p>
								<p class="paragraph">
									模型对各个样本预测结果见
									<a href="@(resultPath)Treatment/@(data.treats(i))/@(data.resultDatas(i).diagnoseDir)/01_Random_Forest/Diagnose_Model_Generation/Logistic_Regression/LR_Prediction.csv">
										LR_Prediction.csv
									</a>
									<a href="@(resultPath)Treatment/@(data.treats(i))/@(data.resultDatas(i).diagnoseDir)/01_Random_Forest/Diagnose_Model_Generation/Logistic_Regression" target="_blank"><span class="fa fa-folder-open"></span></a>

								</p>
								<p class="paragraph">
									模型中各个代谢物的重要性见
									<a href="@(resultPath)Treatment/@(data.treats(i))/@(data.resultDatas(i).diagnoseDir)/01_Random_Forest/Diagnose_Model_Generation/Logistic_Regression/LR_VarImp.csv">
										LR_VarImp.csv
									</a>
									<a href="@(resultPath)Treatment/@(data.treats(i))/@(data.resultDatas(i).diagnoseDir)/01_Random_Forest/Diagnose_Model_Generation/Logistic_Regression" target="_blank"><span class="fa fa-folder-open"></span></a>

								</p>
								<p class="paragraph">
									ROC图见
									<a href="@(resultPath)Treatment/@(data.treats(i))/@(data.resultDatas(i).diagnoseDir)/01_Random_Forest/Diagnose_Model_Generation/Logistic_Regression/ROC_Curve.pdf" target="_blank">
										ROC_Curve.pdf
									</a>
									<a href="@(resultPath)Treatment/@(data.treats(i))/@(data.resultDatas(i).diagnoseDir)/01_Random_Forest/Diagnose_Model_Generation/Logistic_Regression" target="_blank"><span class="fa fa-folder-open"></span></a>

								</p>
								<p class="paragraph">
									PR曲线见
									<a href="@(resultPath)Treatment/@(data.treats(i))/@(data.resultDatas(i).diagnoseDir)/01_Random_Forest/Diagnose_Model_Generation/Logistic_Regression/PR_Curve.pdf" target="_blank">
										PR_Curve.pdf
									</a>
									<a href="@(resultPath)Treatment/@(data.treats(i))/@(data.resultDatas(i).diagnoseDir)/01_Random_Forest/Diagnose_Model_Generation/Logistic_Regression" target="_blank"><span class="fa fa-folder-open"></span></a>

								</p>
							</div>

							<div id="page">
								<br/>
								@user.report.rf(resultPath, i)
								<p class="paragraph">
									采用Random Forest模型，利用生物标志物数据和分组数据进行建模预测，结果如下：
								</p>
								<p class="paragraph">
									模型结果见文件夹<a target="_blank" href="@(resultPath)Treatment/@(data.treats(i))/@(data.resultDatas(i).diagnoseDir)/01_Random_Forest/Diagnose_Model_Generation/Random_Forest">
									Random_Forest</a>
								</p>
								<p class="paragraph">
									模型对各个样本预测结果见
									<a href="@(resultPath)Treatment/@(data.treats(i))/@(data.resultDatas(i).diagnoseDir)/01_Random_Forest/Diagnose_Model_Generation/Random_Forest/RF_Prediction.csv">
										RF_Prediction.csv
									</a>
									<a href="@(resultPath)Treatment/@(data.treats(i))/@(data.resultDatas(i).diagnoseDir)/01_Random_Forest/Diagnose_Model_Generation/Random_Forest" target="_blank"><span class="fa fa-folder-open"></span></a>

								</p>
								<p class="paragraph">
									模型中各个代谢物的重要性见
									<a href="@(resultPath)Treatment/@(data.treats(i))/@(data.resultDatas(i).diagnoseDir)/01_Random_Forest/Diagnose_Model_Generation/Random_Forest/RF_VarImp.csv">
										RF_VarImp.csv
									</a>
									<a href="@(resultPath)Treatment/@(data.treats(i))/@(data.resultDatas(i).diagnoseDir)/01_Random_Forest/Diagnose_Model_Generation/Random_Forest" target="_blank"><span class="fa fa-folder-open"></span></a>

								</p>
								<p class="paragraph">
									ROC图见
									<a href="@(resultPath)Treatment/@(data.treats(i))/@(data.resultDatas(i).diagnoseDir)/01_Random_Forest/Diagnose_Model_Generation/Random_Forest/ROC_Curve.pdf" target="_blank">
										ROC_Curve.pdf
									</a>
									<a href="@(resultPath)Treatment/@(data.treats(i))/@(data.resultDatas(i).diagnoseDir)/01_Random_Forest/Diagnose_Model_Generation/Random_Forest" target="_blank"><span class="fa fa-folder-open"></span></a>

								</p>
								<p class="paragraph">
									PR曲线见
									<a href="@(resultPath)Treatment/@(data.treats(i))/@(data.resultDatas(i).diagnoseDir)/01_Random_Forest/Diagnose_Model_Generation/Random_Forest/PR_Curve.pdf" target="_blank">
										PR_Curve.pdf
									</a>
									<a href="@(resultPath)Treatment/@(data.treats(i))/@(data.resultDatas(i).diagnoseDir)/01_Random_Forest/Diagnose_Model_Generation/Random_Forest" target="_blank"><span class="fa fa-folder-open"></span></a>

								</p>
							</div>

							<div id="page">
								<br/>
								<h5 class="titleLevel5">@(i + 1).@(getDiagnose2Index(i)).1.3 Gradient Boosting模型</h5>

								<p class="paragraph">
									采用Gradient Boosting模型，利用生物标志物数据和分组数据进行建模预测，结果如下：
								</p>
								<p class="paragraph">
									模型结果见文件夹<a target="_blank" href="@(resultPath)Treatment/@(data.treats(i))/@(data.resultDatas(i).diagnoseDir)/01_Random_Forest/Diagnose_Model_Generation/Gradient_Boosting">
									Gradient_Boosting</a>
								</p>
								<p class="paragraph">
									模型对各个样本预测结果见
									<a href="@(resultPath)Treatment/@(data.treats(i))/@(data.resultDatas(i).diagnoseDir)/01_Random_Forest/Diagnose_Model_Generation/Gradient_Boosting/GB_Prediction.csv">
										GB_Prediction.csv
									</a>
									<a href="@(resultPath)Treatment/@(data.treats(i))/@(data.resultDatas(i).diagnoseDir)/01_Random_Forest/Diagnose_Model_Generation/Gradient_Boosting" target="_blank"><span class="fa fa-folder-open"></span></a>

								</p>
								<p class="paragraph">
									模型中各个代谢物的重要性见
									<a href="@(resultPath)Treatment/@(data.treats(i))/@(data.resultDatas(i).diagnoseDir)/01_Random_Forest/Diagnose_Model_Generation/Gradient_Boosting/GB_VarImp.csv">
										GB_VarImp.csv
									</a>
									<a href="@(resultPath)Treatment/@(data.treats(i))/@(data.resultDatas(i).diagnoseDir)/01_Random_Forest/Diagnose_Model_Generation/Gradient_Boosting" target="_blank"><span class="fa fa-folder-open"></span></a>

								</p>
								<p class="paragraph">
									ROC图见
									<a href="@(resultPath)Treatment/@(data.treats(i))/@(data.resultDatas(i).diagnoseDir)/01_Random_Forest/Diagnose_Model_Generation/Gradient_Boosting/ROC_Curve.pdf" target="_blank">
										ROC_Curve.pdf
									</a>
									<a href="@(resultPath)Treatment/@(data.treats(i))/@(data.resultDatas(i).diagnoseDir)/01_Random_Forest/Diagnose_Model_Generation/Gradient_Boosting" target="_blank"><span class="fa fa-folder-open"></span></a>

								</p>
								<p class="paragraph">
									PR曲线见
									<a href="@(resultPath)Treatment/@(data.treats(i))/@(data.resultDatas(i).diagnoseDir)/01_Random_Forest/Diagnose_Model_Generation/Gradient_Boosting/PR_Curve.pdf" target="_blank">
										PR_Curve.pdf
									</a>
									<a href="@(resultPath)Treatment/@(data.treats(i))/@(data.resultDatas(i).diagnoseDir)/01_Random_Forest/Diagnose_Model_Generation/Gradient_Boosting" target="_blank"><span class="fa fa-folder-open"></span></a>

								</p>
							</div>

						} else {
							@user.report.html.noResult()
						}

						<div id="page">
							<br/>
							<h5 class="titleLevel4">@(i + 1).@(getDiagnose2Index(i)).2 采用SVM进行特征选择</h5>
							<p class="paragraph">
								SVM重要性排序前@{data.configDatas(i).svmTop}的差异代谢物得分结果见
								<a href="@(resultPath)Treatment/@(data.treats(i))/@(data.resultDatas(i).diagnoseDir)/02_Support_Vector_Machine/SVM_Top@{data.configDatas(i).svmTop}_Imp_Rank.csv">
									SVM_Top@{data.configDatas(i).svmTop}_Imp_Rank.csv
								</a>
								<a href="@(resultPath)Treatment/@(data.treats(i))/@(data.resultDatas(i).diagnoseDir)/02_Support_Vector_Machine" target="_blank"><span class="fa fa-folder-open"></span></a>

							</p>
							<p class="paragraph">
								SVM重要性排序前@{data.configDatas(i).svmTop}的差异代谢物得分情况见图 @(i + 1)-@getDiagnoseImageIndex(i, 1)
							</p>
							<p class="paragraph">
								SVM全部差异代谢物得分结果见
								<a href="@(resultPath)Treatment/@(data.treats(i))/@(data.resultDatas(i).diagnoseDir)/02_Support_Vector_Machine/SVM_Imp_Rank.csv">
									SVM_Imp_Rank.csv
								</a>
								<a href="@(resultPath)Treatment/@(data.treats(i))/@(data.resultDatas(i).diagnoseDir)/02_Support_Vector_Machine" target="_blank"><span class="fa fa-folder-open"></span></a>

							</p>
							<p class="paragraph">
								SVM全部差异代谢物重要性图见
								<a href="@(resultPath)Treatment/@(data.treats(i))/@(data.resultDatas(i).diagnoseDir)/02_Support_Vector_Machine/SVM_Imp.pdf" target="_blank">
									SVM_Imp.pdf
								</a>
								<a href="@(resultPath)Treatment/@(data.treats(i))/@(data.resultDatas(i).diagnoseDir)/02_Support_Vector_Machine" target="_blank"><span class="fa fa-folder-open"></span></a>

							</p>

							<p class="name_table">　图 @(i + 1)-@getDiagnoseImageIndex(i, 1) SVM评估代谢物重要性</p>
							<p class="center">
								<img class="wid2" src="@(resultPath)Treatment/@(data.treats(i))/@(data.resultDatas(i).diagnoseDir)/02_Support_Vector_Machine/SVM_Top@{data.configDatas(i).svmTop}_Imp.png"/>
							</p>
						</div>

						@if(!data.configDatas(i).isMul) {

							<div id="page">
								<br/>
								<h5 class="titleLevel5">@(i + 1).@(getDiagnose2Index(i)).2.1 Logistic Regression模型</h5>
								<p class="paragraph">
									采用Logistic Regression模型，利用生物标志物数据和分组数据进行建模预测，结果如下：
								</p>
								<p class="paragraph">
									模型结果见文件夹<a target="_blank" href="@(resultPath)Treatment/@(data.treats(i))/@(data.resultDatas(i).diagnoseDir)/02_Support_Vector_Machine/Diagnose_Model_Generation/Logistic_Regression">
									Logistic_Regression</a>
								</p>
								<p class="paragraph">
									模型对各个样本预测结果见
									<a href="@(resultPath)Treatment/@(data.treats(i))/@(data.resultDatas(i).diagnoseDir)/02_Support_Vector_Machine/Diagnose_Model_Generation/Logistic_Regression/LR_Prediction.csv">
										LR_Prediction.csv
									</a>
									<a href="@(resultPath)Treatment/@(data.treats(i))/@(data.resultDatas(i).diagnoseDir)/02_Support_Vector_Machine/Diagnose_Model_Generation/Logistic_Regression" target="_blank"><span class="fa fa-folder-open"></span></a>

								</p>
								<p class="paragraph">
									模型中各个代谢物的重要性见
									<a href="@(resultPath)Treatment/@(data.treats(i))/@(data.resultDatas(i).diagnoseDir)/02_Support_Vector_Machine/Diagnose_Model_Generation/Logistic_Regression/LR_VarImp.csv">
										LR_VarImp.csv
									</a>
									<a href="@(resultPath)Treatment/@(data.treats(i))/@(data.resultDatas(i).diagnoseDir)/02_Support_Vector_Machine/Diagnose_Model_Generation/Logistic_Regression" target="_blank"><span class="fa fa-folder-open"></span></a>

								</p>
								<p class="paragraph">
									ROC图见
									<a href="@(resultPath)Treatment/@(data.treats(i))/@(data.resultDatas(i).diagnoseDir)/02_Support_Vector_Machine/Diagnose_Model_Generation/Logistic_Regression/ROC_Curve.pdf" target="_blank">
										ROC_Curve.pdf
									</a>
									<a href="@(resultPath)Treatment/@(data.treats(i))/@(data.resultDatas(i).diagnoseDir)/02_Support_Vector_Machine/Diagnose_Model_Generation/Logistic_Regression" target="_blank"><span class="fa fa-folder-open"></span></a>

								</p>
								<p class="paragraph">
									PR曲线见
									<a href="@(resultPath)Treatment/@(data.treats(i))/@(data.resultDatas(i).diagnoseDir)/02_Support_Vector_Machine/Diagnose_Model_Generation/Logistic_Regression/PR_Curve.pdf" target="_blank">
										PR_Curve.pdf
									</a>
									<a href="@(resultPath)Treatment/@(data.treats(i))/@(data.resultDatas(i).diagnoseDir)/02_Support_Vector_Machine/Diagnose_Model_Generation/Logistic_Regression" target="_blank"><span class="fa fa-folder-open"></span></a>

								</p>
							</div>

							<div id="page">
								<br/>
								<h5 class="titleLevel5">@(i + 1).@(getDiagnose2Index(i)).2.2 Random Forest模型</h5>
								<p class="paragraph">
									采用Random Forest模型，利用生物标志物数据和分组数据进行建模预测，结果如下：
								</p>
								<p class="paragraph">
									模型结果见文件夹<a target="_blank" href="@(resultPath)Treatment/@(data.treats(i))/@(data.resultDatas(i).diagnoseDir)/02_Support_Vector_Machine/Diagnose_Model_Generation/Random_Forest">
									Random_Forest</a>
								</p>
								<p class="paragraph">
									模型对各个样本预测结果见
									<a href="@(resultPath)Treatment/@(data.treats(i))/@(data.resultDatas(i).diagnoseDir)/02_Support_Vector_Machine/Diagnose_Model_Generation/Random_Forest/RF_Prediction.csv">
										RF_Prediction.csv
									</a>
									<a href="@(resultPath)Treatment/@(data.treats(i))/@(data.resultDatas(i).diagnoseDir)/02_Support_Vector_Machine/Diagnose_Model_Generation/Random_Forest" target="_blank"><span class="fa fa-folder-open"></span></a>

								</p>
								<p class="paragraph">
									模型中各个代谢物的重要性见
									<a href="@(resultPath)Treatment/@(data.treats(i))/@(data.resultDatas(i).diagnoseDir)/02_Support_Vector_Machine/Diagnose_Model_Generation/Random_Forest/RF_VarImp.csv">
										RF_VarImp.csv
									</a>
									<a href="@(resultPath)Treatment/@(data.treats(i))/@(data.resultDatas(i).diagnoseDir)/02_Support_Vector_Machine/Diagnose_Model_Generation/Random_Forest" target="_blank"><span class="fa fa-folder-open"></span></a>

								</p>
								<p class="paragraph">
									ROC图见
									<a href="@(resultPath)Treatment/@(data.treats(i))/@(data.resultDatas(i).diagnoseDir)/02_Support_Vector_Machine/Diagnose_Model_Generation/Random_Forest/ROC_Curve.pdf" target="_blank">
										ROC_Curve.pdf
									</a>
									<a href="@(resultPath)Treatment/@(data.treats(i))/@(data.resultDatas(i).diagnoseDir)/02_Support_Vector_Machine/Diagnose_Model_Generation/Random_Forest" target="_blank"><span class="fa fa-folder-open"></span></a>

								</p>
								<p class="paragraph">
									PR曲线见
									<a href="@(resultPath)Treatment/@(data.treats(i))/@(data.resultDatas(i).diagnoseDir)/02_Support_Vector_Machine/Diagnose_Model_Generation/Random_Forest/PR_Curve.pdf" target="_blank">
										PR_Curve.pdf
									</a>
									<a href="@(resultPath)Treatment/@(data.treats(i))/@(data.resultDatas(i).diagnoseDir)/02_Support_Vector_Machine/Diagnose_Model_Generation/Random_Forest" target="_blank"><span class="fa fa-folder-open"></span></a>

								</p>
							</div>

							<div id="page">
								<br/>
								<h5 class="titleLevel5">@(i + 1).@(getDiagnose2Index(i)).2.3 Gradient Boosting模型</h5>

								<p class="paragraph">
									采用Gradient Boosting模型，利用生物标志物数据和分组数据进行建模预测，结果如下：
								</p>
								<p class="paragraph">
									模型结果见文件夹<a target="_blank" href="@(resultPath)Treatment/@(data.treats(i))/@(data.resultDatas(i).diagnoseDir)/02_Support_Vector_Machine/Diagnose_Model_Generation/Gradient_Boosting">
									Gradient_Boosting</a>
								</p>
								<p class="paragraph">
									模型对各个样本预测结果见
									<a href="@(resultPath)Treatment/@(data.treats(i))/@(data.resultDatas(i).diagnoseDir)/02_Support_Vector_Machine/Diagnose_Model_Generation/Gradient_Boosting/GB_Prediction.csv">
										GB_Prediction.csv
									</a>
									<a href="@(resultPath)Treatment/@(data.treats(i))/@(data.resultDatas(i).diagnoseDir)/02_Support_Vector_Machine/Diagnose_Model_Generation/Gradient_Boosting" target="_blank"><span class="fa fa-folder-open"></span></a>

								</p>
								<p class="paragraph">
									模型中各个代谢物的重要性见
									<a href="@(resultPath)Treatment/@(data.treats(i))/@(data.resultDatas(i).diagnoseDir)/02_Support_Vector_Machine/Diagnose_Model_Generation/Gradient_Boosting/GB_VarImp.csv">
										GB_VarImp.csv
									</a>
									<a href="@(resultPath)Treatment/@(data.treats(i))/@(data.resultDatas(i).diagnoseDir)/02_Support_Vector_Machine/Diagnose_Model_Generation/Gradient_Boosting" target="_blank"><span class="fa fa-folder-open"></span></a>

								</p>
								<p class="paragraph">
									ROC图见
									<a href="@(resultPath)Treatment/@(data.treats(i))/@(data.resultDatas(i).diagnoseDir)/02_Support_Vector_Machine/Diagnose_Model_Generation/Gradient_Boosting/ROC_Curve.pdf" target="_blank">
										ROC_Curve.pdf
									</a>
									<a href="@(resultPath)Treatment/@(data.treats(i))/@(data.resultDatas(i).diagnoseDir)/02_Support_Vector_Machine/Diagnose_Model_Generation/Gradient_Boosting" target="_blank"><span class="fa fa-folder-open"></span></a>

								</p>
								<p class="paragraph">
									PR曲线见
									<a href="@(resultPath)Treatment/@(data.treats(i))/@(data.resultDatas(i).diagnoseDir)/02_Support_Vector_Machine/Diagnose_Model_Generation/Gradient_Boosting/PR_Curve.pdf" target="_blank">
										PR_Curve.pdf
									</a>
									<a href="@(resultPath)Treatment/@(data.treats(i))/@(data.resultDatas(i).diagnoseDir)/02_Support_Vector_Machine/Diagnose_Model_Generation/Gradient_Boosting" target="_blank"><span class="fa fa-folder-open"></span></a>

								</p>
							</div>

						} else {
							@user.report.html.noResult()
						}


						<div id="page">
							<br/>
							<h5 class="titleLevel4">@(i + 1).@(getDiagnose2Index(i)).3 采用Boruta进行特征选择</h5>
							<p class="paragraph">
								使用Boruta对RF和SVM得到的差异代谢物汇总结果进行特征选择结果见
								<a href="@(resultPath)Treatment/@(data.treats(i))/@(data.resultDatas(i).diagnoseDir)/03_Boruta/Decision_Info.csv">
									Decision_Info.csv
								</a>
								<a href="@(resultPath)Treatment/@(data.treats(i))/@(data.resultDatas(i).diagnoseDir)/03_Boruta" target="_blank"><span class="fa fa-folder-open"></span></a>

							</p>
							<p class="paragraph">
								使用Boruta对RF和SVM得到的差异代谢物汇总结果进行特征选择结果如下图
							</p>
							<p class="paragraph">
								RF、SVM、Boruta筛选出的差异代谢物韦恩图见
								<a href="@(resultPath)Treatment/@(data.treats(i))/@(data.resultDatas(i).diagnoseDir)/Venn_Plot.pdf" target="_blank">
									Venn_Plot.pdf
								</a>
								<a href="@(resultPath)Treatment/@(data.treats(i))/@(data.resultDatas(i).diagnoseDir)/03_Boruta" target="_blank"><span class="fa fa-folder-open"></span></a>

							</p>

							<p class="name_table">　图 @(i + 1)-@getDiagnoseImageIndex(i, 2) Boruta评估代谢物重要性</p>
							<p class="center">
								<img class="wid2" src="@(resultPath)Treatment/@(data.treats(i))/@(data.resultDatas(i).diagnoseDir)/03_Boruta/Decision_Boxplot.png"/>
							</p>
							<p class="paragraph">
								图中绿色标注的已确认（“Comfirmed”）的特征可作为生物标志物进一步建模分析。后续的Logistic Regression模型、Random Forest模型和Gradient Boosting模型均是使用Boruta结果中Comfirmed的代谢物进行的。
							</p>
						</div>

						@if(!data.configDatas(i).isMul) {

							<div id="page">
								<br/>
								<h5 class="titleLevel5">@(i + 1).@(getDiagnose2Index(i)).3.1 Logistic Regression模型</h5>
								<p class="paragraph">
									采用Logistic Regression模型，利用生物标志物数据和分组数据进行建模预测，结果如下：
								</p>
								<p class="paragraph">
									模型结果见文件夹<a target="_blank" href="@(resultPath)Treatment/@(data.treats(i))/@(data.resultDatas(i).diagnoseDir)/03_Boruta/Diagnose_Model_Generation/Logistic_Regression">
									Logistic_Regression</a>
								</p>
								<p class="paragraph">
									模型对各个样本预测结果见
									<a href="@(resultPath)Treatment/@(data.treats(i))/@(data.resultDatas(i).diagnoseDir)/03_Boruta/Diagnose_Model_Generation/Logistic_Regression/LR_Prediction.csv">
										LR_Prediction.csv
									</a>
									<a href="@(resultPath)Treatment/@(data.treats(i))/@(data.resultDatas(i).diagnoseDir)/03_Boruta/Diagnose_Model_Generation/Logistic_Regression" target="_blank"><span class="fa fa-folder-open"></span></a>

								</p>
								<p class="paragraph">
									模型中各个代谢物的重要性见
									<a href="@(resultPath)Treatment/@(data.treats(i))/@(data.resultDatas(i).diagnoseDir)/03_Boruta/Diagnose_Model_Generation/Logistic_Regression/LR_VarImp.csv">
										LR_VarImp.csv
									</a>
									<a href="@(resultPath)Treatment/@(data.treats(i))/@(data.resultDatas(i).diagnoseDir)/03_Boruta/Diagnose_Model_Generation/Logistic_Regression" target="_blank"><span class="fa fa-folder-open"></span></a>

								</p>
								<p class="paragraph">
									ROC图见
									<a href="@(resultPath)Treatment/@(data.treats(i))/@(data.resultDatas(i).diagnoseDir)/03_Boruta/Diagnose_Model_Generation/Logistic_Regression/ROC_Curve.pdf" target="_blank">
										ROC_Curve.pdf
									</a>
									<a href="@(resultPath)Treatment/@(data.treats(i))/@(data.resultDatas(i).diagnoseDir)/03_Boruta/Diagnose_Model_Generation/Logistic_Regression" target="_blank"><span class="fa fa-folder-open"></span></a>

								</p>
								<p class="paragraph">
									PR曲线见
									<a href="@(resultPath)Treatment/@(data.treats(i))/@(data.resultDatas(i).diagnoseDir)/03_Boruta/Diagnose_Model_Generation/Logistic_Regression/PR_Curve.pdf" target="_blank">
										PR_Curve.pdf
									</a>
									<a href="@(resultPath)Treatment/@(data.treats(i))/@(data.resultDatas(i).diagnoseDir)/03_Boruta/Diagnose_Model_Generation/Logistic_Regression" target="_blank"><span class="fa fa-folder-open"></span></a>

								</p>
							</div>

							<div id="page">
								<br/>
								<h5 class="titleLevel5">@(i + 1).@(getDiagnose2Index(i)).3.2 Random Forest模型</h5>
								<p class="paragraph">
									采用Random Forest模型，利用生物标志物数据和分组数据进行建模预测，结果如下：
								</p>
								<p class="paragraph">
									模型结果见文件夹<a target="_blank" href="@(resultPath)Treatment/@(data.treats(i))/@(data.resultDatas(i).diagnoseDir)/03_Boruta/Diagnose_Model_Generation/Random_Forest">
									Random_Forest</a>
								</p>
								<p class="paragraph">
									模型对各个样本预测结果见
									<a href="@(resultPath)Treatment/@(data.treats(i))/@(data.resultDatas(i).diagnoseDir)/03_Boruta/Diagnose_Model_Generation/Random_Forest/RF_Prediction.csv">
										RF_Prediction.csv
									</a>
									<a href="@(resultPath)Treatment/@(data.treats(i))/@(data.resultDatas(i).diagnoseDir)/03_Boruta/Diagnose_Model_Generation/Random_Forest" target="_blank"><span class="fa fa-folder-open"></span></a>

								</p>
								<p class="paragraph">
									模型中各个代谢物的重要性见
									<a href="@(resultPath)Treatment/@(data.treats(i))/@(data.resultDatas(i).diagnoseDir)/03_Boruta/Diagnose_Model_Generation/Random_Forest/RF_VarImp.csv">
										RF_VarImp.csv
									</a>
									<a href="@(resultPath)Treatment/@(data.treats(i))/@(data.resultDatas(i).diagnoseDir)/03_Boruta/Diagnose_Model_Generation/Random_Forest" target="_blank"><span class="fa fa-folder-open"></span></a>

								</p>
								<p class="paragraph">
									ROC图见
									<a href="@(resultPath)Treatment/@(data.treats(i))/@(data.resultDatas(i).diagnoseDir)/03_Boruta/Diagnose_Model_Generation/Random_Forest/ROC_Curve.pdf" target="_blank">
										ROC_Curve.pdf
									</a>
									<a href="@(resultPath)Treatment/@(data.treats(i))/@(data.resultDatas(i).diagnoseDir)/03_Boruta/Diagnose_Model_Generation/Random_Forest" target="_blank"><span class="fa fa-folder-open"></span></a>

								</p>
								<p class="paragraph">
									PR曲线见
									<a href="@(resultPath)Treatment/@(data.treats(i))/@(data.resultDatas(i).diagnoseDir)/03_Boruta/Diagnose_Model_Generation/Random_Forest/PR_Curve.pdf" target="_blank">
										PR_Curve.pdf
									</a>
									<a href="@(resultPath)Treatment/@(data.treats(i))/@(data.resultDatas(i).diagnoseDir)/03_Boruta/Diagnose_Model_Generation/Random_Forest" target="_blank"><span class="fa fa-folder-open"></span></a>

								</p>
							</div>

							<div id="page">
								<br/>
								<h5 class="titleLevel5">@(i + 1).@(getDiagnose2Index(i)).3.3 Gradient Boosting模型</h5>

								<p class="paragraph">
									采用Gradient Boosting模型，利用生物标志物数据和分组数据进行建模预测，结果如下：
								</p>
								<p class="paragraph">
									模型结果见文件夹<a target="_blank" href="@(resultPath)Treatment/@(data.treats(i))/@(data.resultDatas(i).diagnoseDir)/03_Boruta/Diagnose_Model_Generation/Gradient_Boosting">
									Gradient_Boosting</a>
								</p>
								<p class="paragraph">
									模型对各个样本预测结果见
									<a href="@(resultPath)Treatment/@(data.treats(i))/@(data.resultDatas(i).diagnoseDir)/03_Boruta/Diagnose_Model_Generation/Gradient_Boosting/GB_Prediction.csv">
										GB_Prediction.csv
									</a>
									<a href="@(resultPath)Treatment/@(data.treats(i))/@(data.resultDatas(i).diagnoseDir)/03_Boruta/Diagnose_Model_Generation/Gradient_Boosting" target="_blank"><span class="fa fa-folder-open"></span></a>

								</p>
								<p class="paragraph">
									模型中各个代谢物的重要性见
									<a href="@(resultPath)Treatment/@(data.treats(i))/@(data.resultDatas(i).diagnoseDir)/03_Boruta/Diagnose_Model_Generation/Gradient_Boosting/GB_VarImp.csv">
										GB_VarImp.csv
									</a>
									<a href="@(resultPath)Treatment/@(data.treats(i))/@(data.resultDatas(i).diagnoseDir)/03_Boruta/Diagnose_Model_Generation/Gradient_Boosting" target="_blank"><span class="fa fa-folder-open"></span></a>

								</p>
								<p class="paragraph">
									ROC图见
									<a href="@(resultPath)Treatment/@(data.treats(i))/@(data.resultDatas(i).diagnoseDir)/03_Boruta/Diagnose_Model_Generation/Gradient_Boosting/ROC_Curve.pdf" target="_blank">
										ROC_Curve.pdf
									</a>
									<a href="@(resultPath)Treatment/@(data.treats(i))/@(data.resultDatas(i).diagnoseDir)/03_Boruta/Diagnose_Model_Generation/Gradient_Boosting" target="_blank"><span class="fa fa-folder-open"></span></a>

								</p>
								<p class="paragraph">
									PR曲线见
									<a href="@(resultPath)Treatment/@(data.treats(i))/@(data.resultDatas(i).diagnoseDir)/03_Boruta/Diagnose_Model_Generation/Gradient_Boosting/PR_Curve.pdf" target="_blank">
										PR_Curve.pdf
									</a>
									<a href="@(resultPath)Treatment/@(data.treats(i))/@(data.resultDatas(i).diagnoseDir)/03_Boruta/Diagnose_Model_Generation/Gradient_Boosting" target="_blank"><span class="fa fa-folder-open"></span></a>

								</p>
							</div>

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					}

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				@if(info.isHuiyun) {
					@user.report.html.huiyunIntro(path, index2Chinese(getIntroIndex))
				}
				@if(info.isMet) {
					@user.report.html.metIntro(path, index2Chinese(getIntroIndex))
				}
				@if(info.isHuiyunLab) {
					@user.report.html.huiyunLabIntro(path, index2Chinese(getIntroIndex))
				}
				@if(info.isLiuyuan) {
					@user.report.html.liuyuanIntro(path, index2Chinese(getIntroIndex))
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